app.py

import gradio as gr
from pyvis.network import Network
import networkx as nx
import numpy as np
import pandas as pd
import os
from datasets import load_dataset
from datasets import Features
from datasets import Value
from datasets import Dataset
import matplotlib.pyplot as plt
import re
from collections import defaultdict
from huggingface_hub import hf_hub_download
import json        
pattern = r'"(.*?)"'
# this pattern captures anything in a double quotes. 

Secret_token = os.getenv('HF_token')

dataset = load_dataset('FDSRashid/hadith_info',data_files = 'Basic_Edge_Information.csv', token = Secret_token, split = 'train')

edge_info = dataset.to_pandas()

features = Features({'Rawi ID': Value('int32'), 'Famous Name': Value('string'), 'Narrator Rank': Value('string'), 'Number of Narrations': Value('string'),  'Generation': Value('string')})
narrator_bios = load_dataset("FDSRashid/hadith_info", data_files = 'Teacher_Bios.csv', token = Secret_token,features=features )
narrator_bios = narrator_bios['train'].to_pandas()
narrator_bios.loc[49845, 'Narrator Rank'] = 'رسول الله'
narrator_bios.loc[49845, 'Number of Narrations'] = 0
narrator_bios['Number of Narrations'] = narrator_bios['Number of Narrations'].astype(int)
narrator_bios.loc[49845, 'Number of Narrations'] = 327512
# 8125 Narrators have no Generation, listed in dataset as None
narrator_bios['Generation'] = narrator_bios['Generation'].replace([None], [-1])
narrator_bios['Generation'] = narrator_bios['Generation'].astype(int)

features = Features({'matn': Value('string'), 'taraf_ID': Value('string'), 'bookid_hadithid': Value('string')})

dataset = load_dataset("FDSRashid/hadith_info", data_files = 'All_Matns.csv',token = Secret_token, features = features)
matn_info = dataset['train'].to_pandas()
matn_info = matn_info.drop(97550)
matn_info = matn_info.drop(307206)
matn_info['taraf_ID'] = matn_info['taraf_ID'].replace('KeyAbsent', -1)

matn_info['taraf_ID'] = matn_info['taraf_ID'].astype(int)

# Isnad Info Hadiths column is structured like {"BookNum_HadithNum", ...} for each edge
isnad_info = load_dataset('FDSRashid/hadith_info',token = Secret_token, data_files = 'isnad_info.csv', split = 'train').to_pandas()
isnad_info['Hadiths Cleaned'] = isnad_info['Hadiths'].apply(lambda x: [re.findall(pattern, string)[0].split("_") for string in x[1:-1].split(',')])
# Hadiths Cleaned is a list of lists, each sub-list is Book Id, Hadith ID
taraf_max = np.max(matn_info['taraf_ID'].unique())
isnad_info['Tarafs Cleaned'] = isnad_info['Tarafs'].apply(lambda x: np.array([int(i.strip(' ')) for i in x[1:-1].split(',')]))

cmap = plt.colormaps['cool']

books = load_dataset('FDSRashid/Hadith_info', data_files='Books.csv', token = Secret_token)['train'].to_pandas()

matn_info['Book_ID'] = matn_info['bookid_hadithid'].apply(lambda x: int(x.split('_')[0]))
matn_info['Hadith Number'] = matn_info['bookid_hadithid'].apply(lambda x: int(x.split('_')[1]))
matn_info = pd.merge(matn_info, books, on='Book_ID')


from huggingface_hub import hf_hub_download

# Download and read a file
file_path = hf_hub_download(
    repo_id="FDSRashid/hadith_info",  # read in fast lookup data structure
    filename="hadith_lookup.json",
    repo_type="dataset",
    token=Secret_token,
)

with open(file_path, 'r') as f:
    hadith_lookup_dict = json.load(f)
hadith_lookup = defaultdict(list, hadith_lookup_dict)

    
def value_to_hex(value):
    rgba_color = cmap(value)
    return "#{:02X}{:02X}{:02X}".format(int(rgba_color[0] * 255), int(rgba_color[1] * 255), int(rgba_color[2] * 255))


def get_node_info(node):
    node_info = narrator_bios[narrator_bios['Rawi ID'] == int(node)]
    # Extract values with defaults, using `.iloc` if available, otherwise default
    student_narrations = node_info['Number of Narrations'].iloc[0] if not node_info.empty else 1
    student_gen = node_info['Generation'].iloc[0] if not node_info.empty else -1
    student_rank = node_info['Narrator Rank'].iloc[0] if not node_info.empty else 'فلان'
    node_name = node_info['Famous Name'].iloc[0] if not node_info.empty else 'فلان'
    return node_info,student_narrations,student_gen, student_rank, node_name
    

def visualize_isnad(taraf_num, yaxis):
    # Precompute filtered dataframes
    taraf = matn_info[matn_info['taraf_ID'] == taraf_num]
    taraf_hadith = taraf['bookid_hadithid'].to_list()
    
    
    # Precompute hadiths where taraf_num exists
    hadith_cleaned = isnad_info['Tarafs Cleaned'].apply(lambda x: taraf_num in x)
    isnad_hadith = isnad_info[hadith_cleaned]
    
    lst_hadith = []
    
    for i, hadith_parts in enumerate(taraf_hadith):
        # look up hadith for each bookid_hadithid
        isnad_hadith1 = isnad_info.iloc[hadith_lookup[taraf_hadith[i]]][['Source', 'Destination']]
        
        # Create graph and find end nodes
        G = nx.from_pandas_edgelist(isnad_hadith1, source='Source', target='Destination', create_using=nx.DiGraph())
        nodes = [int(n) for n, d in G.out_degree() if d == 0]
        
        if nodes:
            # Batch fetch data from narrator_bios for efficiency
            bio_data = narrator_bios[narrator_bios['Rawi ID'].isin(nodes)]
            
            for n in nodes:
                gen_node = bio_data.loc[bio_data['Rawi ID'] == n, 'Generation'].squeeze()
                gen_node = gen_node if pd.notna(gen_node) else -1
                
                name_node = bio_data.loc[bio_data['Rawi ID'] == n, 'Famous Name'].squeeze()
                name_node = name_node if pd.notna(name_node) else 'فلان'
                
                # Append result for each node
                lst_hadith.append([
                    taraf.iloc[i]['matn'],
                    gen_node,
                    name_node,
                    taraf.iloc[i]['Book_Name'],
                    taraf.iloc[i]['Author'],
                    taraf.iloc[i]['Hadith Number'],
                    str(n),
                    i
                ])
    
    # Convert to DataFrame
    df = pd.DataFrame(lst_hadith, columns=['Matn', 'Generation', 'Name', 'Book_Name', 'Author', 'Book Hadith Number', 'End Transmitter ID', 'Hadith Number'])
    

    isnad_hadith['Teacher'] = isnad_hadith['Source'].apply(lambda x: narrator_bios[narrator_bios['Rawi ID'].astype(int) == int(x)]['Famous Name'].to_list())
    isnad_hadith['Student'] = isnad_hadith['Destination'].apply(lambda x: narrator_bios[narrator_bios['Rawi ID'].astype(int) == int(x)]['Famous Name'].to_list())
    isnad_hadith['Teacher'] = isnad_hadith['Teacher'].apply(lambda x: x[0] if len(x)==1 else 'فلان')
    isnad_hadith['Student'] = isnad_hadith['Student'].apply(lambda x: x[0] if len(x)==1 else 'فلان')

    end_nodes = df['End Transmitter ID'].tolist() 
    G = nx.from_pandas_edgelist(isnad_hadith, source = 'Source', target = 'Destination', create_using = nx.DiGraph())
    isnad_pos = nx.nx_agraph.graphviz_layout(G, prog='dot')
    x_stretch = 4
    y_stretch = 4
    net = Network(directed =True, select_menu=True, cdn_resources='remote')

    for node, pos in isnad_pos.items():
        node_info,student_narrations,student_gen, student_rank, node_name = get_node_info(node)
        if node == '99999':
            net.add_node(node, font = {'size':50, 'color': 'black'}, color = '#000000', label = f'{node_name} \n ID: {node} - Gen {student_gen}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 70)   
        elif node in end_nodes:
            end_matn_info = df[df["End Transmitter ID"] == node]
            net.add_node(node, font = {'size':30, 'color': 'red'}, color = value_to_hex(student_narrations), label = f'{node_name} \n {student_rank} \n ID: {node} - Gen {student_gen} \n Hadith {" ".join(end_matn_info["Hadith Number"].astype("string").tolist())}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 50)
        else:
            net.add_node(node, font = {'size':30, 'color': 'red'}, color = value_to_hex(student_narrations), label = f'{node_name} \n {student_rank} \n ID: {node} - Gen {student_gen}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 50)
    for _, row in isnad_hadith.iterrows():
        source = row['Source']
        target = row['Destination']
        net.add_edge(source, target, color = value_to_hex(int(row[f'{yaxis} Count'])), label = f"{row[f'{yaxis} Count']}")
    net.toggle_physics(False)    
    html = net.generate_html()
    html = html.replace("'", "\"")
    df = df.rename(columns = {'Generation': 'Gen.', 'Book Hadith Number': 'Hdth Num', 'End Transmitter ID': 'End Narrator ID', 'Hadith Number': 'Index', 'Book_Name': 'Book', 'Name':'Final Narrator'})
    return f"""<iframe style="width: 100%; height: 600px;margin:0 auto" name="result" allow="midi; geolocation; microphone; camera; 
  display-capture; encrypted-media;" sandbox="allow-modals allow-forms 
  allow-scripts allow-same-origin allow-popups 
  allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
  allowpaymentrequest="" frameborder="0" srcdoc='{html}'></iframe>""" , df.drop('Hdth Num', axis=1)
    

def visualize_subTaraf(taraf_num, hadith_str, yaxis):
    hadith_list = hadith_str.split(',')
    hadith_list = [hadith.strip() for hadith in hadith_list]
    hadiths = np.array([], dtype=int)
    for hadith in hadith_list:
        if '-' in hadith:
            if hadith.count('-') > 1:
                #print('Please use only one Dash mark!')
                raise gr.Error('Please use only one Dash mark!')
            hadith_multi = hadith.strip().split('-')
            if any([not had.isnumeric() for had in hadith_multi]):
                #print('Invalid Begining')
                raise gr.Error('Invalid Begining')
            elif len(hadith_multi) != 2:
                #print('Two numbers for a range of Hadith numbers please!')
                raise gr.Error('Two numbers for a range of Hadith numbers please!')
            hadith_multi = [int(had) for had in hadith_multi]
            hadiths = np.append(hadiths, np.arange(hadith_multi[0], hadith_multi[1] +1))
        elif hadith.isnumeric():
            hadiths = np.append(hadiths, int(hadith))
        else:
            #print('Invalid Data format!')
            raise gr.Error("Invalid Data format!")

    hadiths= np.unique(hadiths)
    taraf = matn_info[matn_info['taraf_ID'] == taraf_num]
    num_hadith = taraf.shape[0]
    if np.max(hadiths) > num_hadith:
        raise gr.Error(f'Hadith index outside of range. Total Number of Hadith in this Taraf: {num_hadith}')
    taraf['Index'] = np.arange(num_hadith)
    sub_taraf = taraf[taraf['Index'].isin(hadiths)]

    taraf_hadith = sub_taraf['bookid_hadithid'].to_list()
    taraf_hadith_split = [i.split('_') for i in taraf_hadith]

    hadith_cleaned = isnad_info['Tarafs Cleaned'].apply(lambda x: taraf_num in x)
    isnad_hadith = isnad_info[hadith_cleaned]
    subtaraf_filter = isnad_info['Hadiths Cleaned'].apply(lambda x: any(i in x for i in taraf_hadith_split))
    isnad_hadith = isnad_hadith[subtaraf_filter]
    
    isnad_hadith['Teacher'] = isnad_hadith['Source'].apply(lambda x: narrator_bios[narrator_bios['Rawi ID'].astype(int) == int(x)]['Famous Name'].to_list())
    isnad_hadith['Student'] = isnad_hadith['Destination'].apply(lambda x: narrator_bios[narrator_bios['Rawi ID'].astype(int) == int(x)]['Famous Name'].to_list())
    isnad_hadith['Teacher'] = isnad_hadith['Teacher'].apply(lambda x: x[0] if len(x)==1 else 'فلان')
    isnad_hadith['Student'] = isnad_hadith['Student'].apply(lambda x: x[0] if len(x)==1 else 'فلان')

    G = nx.from_pandas_edgelist(isnad_hadith, source = 'Source', target = 'Destination', create_using = nx.DiGraph())
    isnad_pos = nx.nx_agraph.graphviz_layout(G, prog='dot')
    x_stretch = 4
    y_stretch = 4
    net = Network(directed =True, select_menu=True, cdn_resources='remote')

    for node, pos in isnad_pos.items():
        node_info,student_narrations,student_gen, student_rank, node_name = get_node_info(node)
        if node == '99999':
            net.add_node(node, font = {'size':50, 'color': 'black'}, color = '#000000', label = f'{node_name} \n ID: {node} - Gen {student_gen}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 70)   
        else:
            net.add_node(node, font = {'size':30, 'color': 'red'}, color = value_to_hex(student_narrations), label = f'{node_name} \n {student_rank} \n ID: {node} - Gen {student_gen}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 50)
    for _, row in isnad_hadith.iterrows():
        source = row['Source']
        target = row['Destination']
        net.add_edge(source, target, color = value_to_hex(int(row[f'{yaxis} Count'])), label = f"{row[f'{yaxis} Count']}")
    net.toggle_physics(False)    
    html = net.generate_html()
    html = html.replace("'", "\"")
    return f"""<iframe style="width: 100%; height: 600px;margin:0 auto" name="result" allow="midi; geolocation; microphone; camera; 
  display-capture; encrypted-media;" sandbox="allow-modals allow-forms 
  allow-scripts allow-same-origin allow-popups 
  allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
  allowpaymentrequest="" frameborder="0" srcdoc='{html}'></iframe>""", sub_taraf[['matn', 'Book_Name', 'Author', 'Book_ID', 'Hadith Number']]

def taraf_booknum(taraf_num):
    taraf = matn_info[matn_info['taraf_ID'] == taraf_num]
    num_hadith = taraf.shape[0]
    taraf['Index'] = np.arange(num_hadith)
    return taraf[['matn', 'Book_ID', 'Hadith Number', 'Book_Name', 'Author', 'Index']]

def visualize_hadith_isnad(df, yaxis):
    df['bookid_hadithid'] = df['Book_ID'].astype(str) + '_' + df['Hadith Number'].astype(str)
    hadith = matn_info[matn_info['bookid_hadithid'].isin(df['bookid_hadithid'])]
    taraf_hadith_split = [i.split('_') for i in hadith['bookid_hadithid'].to_list()]
    hadith_cleaned = isnad_info['Hadiths Cleaned'].apply(lambda x: any(i in x for i in taraf_hadith_split))
    isnad_hadith = isnad_info[hadith_cleaned]
    isnad_hadith['Teacher'] = isnad_hadith['Source'].apply(lambda x: narrator_bios[narrator_bios['Rawi ID'].astype(int) == int(x)]['Famous Name'].to_list())
    isnad_hadith['Student'] = isnad_hadith['Destination'].apply(lambda x: narrator_bios[narrator_bios['Rawi ID'].astype(int) == int(x)]['Famous Name'].to_list())
    isnad_hadith['Teacher'] = isnad_hadith['Teacher'].apply(lambda x: x[0] if len(x)==1 else 'فلان')
    isnad_hadith['Student'] = isnad_hadith['Student'].apply(lambda x: x[0] if len(x)==1 else 'فلان')

    G = nx.from_pandas_edgelist(isnad_hadith, source = 'Source', target = 'Destination', create_using = nx.DiGraph())
    isnad_pos = nx.nx_agraph.graphviz_layout(G, prog='dot')
    x_stretch = 4
    y_stretch = 4
    net = Network(directed =True, select_menu=True, cdn_resources='remote')

    for node, pos in isnad_pos.items():
        node_info,student_narrations,student_gen, student_rank, node_name = get_node_info(node)
        if node == '99999':
            net.add_node(node, font = {'size':50, 'color': 'black'}, color = '#000000', label = f'{node_name} \n ID: {node} - Gen {student_gen}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 70)   
        else:
            net.add_node(node, font = {'size':30, 'color': 'red'}, color = value_to_hex(student_narrations), label = f'{node_name} \n {student_rank} \n ID: {node} - Gen {student_gen}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 50)
    for _, row in isnad_hadith.iterrows():
        source = row['Source']
        target = row['Destination']
        net.add_edge(source, target, color = value_to_hex(int(row[f'{yaxis} Count'])), label = f"{row[f'{yaxis} Count']}")
    net.toggle_physics(False)    
    html = net.generate_html()
    html = html.replace("'", "\"")
    return f"""<iframe style="width: 100%; height: 600px;margin:0 auto" name="result" allow="midi; geolocation; microphone; camera; 
  display-capture; encrypted-media;" sandbox="allow-modals allow-forms 
  allow-scripts allow-same-origin allow-popups 
  allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
  allowpaymentrequest="" frameborder="0" srcdoc='{html}'></iframe>""" , hadith[['matn', 'Book_ID', 'Hadith Number', 'Book_Name', 'Author', 'taraf_ID']]


def visualize_narrator_taraf(taraf_num, narrator, yaxis):
    taraf = matn_info[matn_info['taraf_ID'] == taraf_num]
    num_hadith = taraf.shape[0]
    taraf['Index'] = np.arange(num_hadith)
    hadith_cleaned = isnad_info['Tarafs Cleaned'].apply(lambda x: taraf_num in x)
    isnad_hadith = isnad_info[hadith_cleaned]
    isnad_hadith['Teacher'] = isnad_hadith['Source'].apply(lambda x: narrator_bios[narrator_bios['Rawi ID'].astype(int) == int(x)]['Famous Name'].to_list())
    isnad_hadith['Student'] = isnad_hadith['Destination'].apply(lambda x: narrator_bios[narrator_bios['Rawi ID'].astype(int) == int(x)]['Famous Name'].to_list())
    isnad_hadith['Teacher'] = isnad_hadith['Teacher'].apply(lambda x: x[0] if len(x)==1 else 'فلان')
    isnad_hadith['Student'] = isnad_hadith['Student'].apply(lambda x: x[0] if len(x)==1 else 'فلان')
    taraf_hadith = taraf['bookid_hadithid'].to_list()
    taraf_hadith_split = [i.split('_') for i in taraf_hadith]

    G = nx.from_pandas_edgelist(isnad_hadith, source = 'Source', target = 'Destination', create_using = nx.DiGraph())
    if narrator not in G.nodes():
        raise gr.Error('Narrator not in Isnad of Taraf!')
    # nodes = list(G.nodes)
    # nodes = [node for node in nodes if node not in list(nx.ancestors(G, narrator)) + list(nx.descendants(G, narrator)) and node !=narrator]
    # [G.remove_nodes_from(nodes)]
    matns_with_narrator = []
    end_node = {}
    for i in range(len(taraf_hadith_split)):
        isnad_in_hadith1 = isnad_hadith['Hadiths Cleaned'].apply(lambda x: taraf_hadith_split[i] in x )
        isnad_hadith1 = isnad_hadith[isnad_in_hadith1]
        G1 = nx.from_pandas_edgelist(isnad_hadith1, source = 'Source', target = 'Destination', create_using = nx.DiGraph())
        node = [n for n, d in G1.out_degree() if d == 0]
        if narrator in list(G1.nodes):
            matns_with_narrator.append(taraf_hadith[i])
            for n in node:
                if n not in list(end_node.keys()):
                    end_node[n] = []
                end_node[n].append(str(i))

    matns_with_narrator_split = [i.split('_') for i in matns_with_narrator]
    hadith_cleaned = isnad_hadith['Hadiths Cleaned'].apply(lambda x: any(i in x for i in matns_with_narrator_split))
    isnad_hadith = isnad_hadith[hadith_cleaned]
    G = nx.from_pandas_edgelist(isnad_hadith, source = 'Source', target = 'Destination', create_using = nx.DiGraph())
    isnad_pos = nx.nx_agraph.graphviz_layout(G, prog='dot')

    narrator_matn_info = taraf[taraf['bookid_hadithid'].isin(matns_with_narrator)]
    num_hadith = narrator_matn_info.shape[0]
    narrator_matn_info['Subset Index'] = np.arange(num_hadith)
    x_stretch = 4
    y_stretch = 4
    net = Network(directed =True, select_menu=True, cdn_resources='remote')

    for node, pos in isnad_pos.items():
        node_info,student_narrations,student_gen, student_rank, node_name = get_node_info(node)
        if node == '99999':
            net.add_node(node, font = {'size':50, 'color': 'black'}, color = '#000000', label = f'{node_name} \n ID: {node} - Gen {student_gen}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 70)   
        elif node in list(end_node.keys()):
            net.add_node(node, font = {'size':30, 'color': 'red'}, color = value_to_hex(student_narrations), label = f'{node_name} \n {student_rank} \n ID: {node} - Gen {student_gen} \n Hadiths {", ".join(end_node[node])}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 50)
        else:
            net.add_node(node, font = {'size':30, 'color': 'red'}, color = value_to_hex(student_narrations), label = f'{node_name} \n {student_rank} \n ID: {node} - Gen {student_gen}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 50)
    for edge in G.edges:
        row = isnad_hadith[(isnad_hadith['Source'] == edge[0]) & (isnad_hadith['Destination'] == edge[1])].iloc[0]
        source = row['Source']
        target = row['Destination']
        net.add_edge(source, target, color = value_to_hex(int(row[f'{yaxis} Count'])), label = f"{row[f'{yaxis} Count']}")
    net.toggle_physics(False)    
    html = net.generate_html()
    html = html.replace("'", "\"")
    return f"""<iframe style="width: 100%; height: 600px;margin:0 auto" name="result" allow="midi; geolocation; microphone; camera; 
  display-capture; encrypted-media;" sandbox="allow-modals allow-forms 
  allow-scripts allow-same-origin allow-popups 
  allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
  allowpaymentrequest="" frameborder="0" srcdoc='{html}'></iframe>""" , narrator_matn_info[['matn', 'Book_Name', 'Author', 'Book_ID', 'Hadith Number', 'Index', 'Subset Index']]


with gr.Blocks() as demo:
    with gr.Tab("Whole Taraf Visualizer"):
        Yaxis = gr.Dropdown(choices = ['Taraf', 'Hadith', 'Isnad', 'Book'], value = 'Taraf', label = 'Variable to Display', info = 'Choose the variable to visualize.')  
        taraf_number = gr.Slider(1,taraf_max , value=10000, label="Taraf", info="Choose the Taraf to Input", step = 1)
        btn = gr.Button('Submit')
        #
        btn.click(fn = visualize_isnad, inputs = [taraf_number, Yaxis], outputs = [gr.HTML(), gr.DataFrame(wrap=True, column_widths=[43, 8, 11,11,10,8, 9])])
    with gr.Tab("Book and Hadith Number Retriever"):
        taraf_num = gr.Slider(1,taraf_max , value=10000, label="Taraf", info="Choose the Taraf to Input", step = 1)
        btn_num = gr.Button('Retrieve')
        btn_num.click(fn=taraf_booknum, inputs = [taraf_num], outputs= [gr.DataFrame(wrap=True)])
    with gr.Tab('Sub Taraf Visualizer'):
        taraf_num = gr.Slider(1,taraf_max , value=10000, label="Taraf", info="Choose the Taraf to Input", step = 1)
        Yaxis = gr.Dropdown(choices = ['Taraf', 'Hadith', 'Isnad', 'Book'], value = 'Taraf', label = 'Variable to Display', info = 'Choose the variable to visualize.') 
        hadith_str = gr.Textbox(label='Hadith Selection', info='Choose which range of Hadith you would like visualized from the Taraf (eg "1, 2, 4-7")')
        btn_sub = gr.Button('Visualize')
        btn_sub.click(fn=visualize_subTaraf, inputs = [taraf_num, hadith_str, Yaxis], outputs=[gr.HTML(), gr.DataFrame(wrap=True)])
    with gr.Tab('Select Hadith Isnad Visualizer'):
        yyaxis = gr.Dropdown(choices = ['Taraf', 'Hadith', 'Isnad', 'Book'], value = 'Taraf', label = 'Variable to Display', info = 'Choose the variable to visualize.')
        hadith_selection =  gr.Dataframe(
            headers=["Book_ID", "Hadith Number"],
            datatype=["number", "number"],
            row_count=5,
            col_count=(2, "fixed"))
        btn_hadith = gr.Button('Visualize')
        btn_hadith.click(fn=visualize_hadith_isnad, inputs=[hadith_selection, yyaxis], outputs=[gr.HTML(), gr.DataFrame(wrap=True)])
    with gr.Tab('Taraf Narrator Isnad Visualizer'):
        Yaxis = gr.Dropdown(choices = ['Taraf', 'Hadith', 'Isnad', 'Book'], value = 'Taraf', label = 'Variable to Display', info = 'Choose the variable to visualize.')  
        taraf_number = gr.Slider(1,taraf_max , value=10000, label="Taraf", info="Choose the Taraf to Input", step = 1)
        narr = gr.Textbox(label='Narrator', info='Choose a Narrator (Refer to full isnad from previous tab)')
        btn_narr = gr.Button('Visualize')
        btn_narr.click(fn=visualize_narrator_taraf, inputs=[taraf_number, narr, Yaxis], outputs=[gr.HTML(), gr.DataFrame(wrap=True)])
demo.launch()